We first develop an efficient algorithm to compute Deltas of interest rate derivatives for a number of standard market models. The computational complexity of the algorithms is shown to be proportional to the number of rates times the number of factors per step. We then show how to extend the method to efficiently compute Vegas in those market models. Content Type Journal ArticlePages 17-33DOI 10.3233/AF-2011-002Authors Mark Joshi, Chao Yang, Journal Algorithmic FinanceOnline ISSN 2157-6203Print ISSN 2158-5571 Journal Volume Volume 1 Journal Issue Volume 1, Number 1 / 2011

Bid and ask sizes at the top of the order book provide information on short-term price moves. Drawing from classical descriptions of the order book in terms of queues and order-arrival rates (Smith et al., 2003), we consider a diffusion model for the evolution of the best bid/ask queues. We compute the probability that the next price move is upward, conditional on the best bid/ask sizes, the hidden liquidity in the market and the correlation between changes in the bid/ask sizes. The model can be useful, among other things, to rank trading venues in terms of the “information content” of their...

Research indicates that individual investors trade excessively and underperform the market indices, Barber and Odean (2000). The purpose of this paper is to help explain which behavioral biases, if any, can explain this result using a simulation approach. Results indicate that putting too much weight on the current environment, anchoring, is the largest factor in explaining individual investor underperformance. In addition, loss aversion is the largest factor to explain excessive trading. When these two biases are combined trading activity and underperformance are heightened. Content Type...

(2094 days ago)

About:

Algorithmic Finance is both a nascent field of study and a new high-quality academic research journal that seeks to bridge computer science and finance. It covers such applications as:

High frequency and algorithmic trading

Statistical arbitrage strategies

Momentum and other algorithmic portfolio management

Machine learning and other aspects of computational financial intelligence

Agent-based finance

Complexity and market efficiency

Algorithmic analysis of derivatives valuation

Behavioral finance examining the algorithms of the investors

Applications of quantum computation to finance

News analytics and automated textual analysis

Instructions to Authors

We are seeking papers on the topics listed above, or, more generally, papers at the intersection of theoretical computer science and either theoretical or empirical finance.

Initial submissions must be in PDF format. There is no submission fee or publication fee. Submissions are double-blind peer reviewed. Your submission may not be under review at any other journal at any time while it is under review at Algorithmic Finance.